People increasingly work with artificial intelligence (AI) tools in fields including medicine, finance, and law, as well as in daily activities such as traveling, shopping, and communicating. These human-AI systems have tremendous potential given the complementary nature of humans and AI - the general intelligence of humans allows us to reason about diverse problems, and the computational power of AI systems allows them to accomplish specific tasks that people find difficult. In fact, a large body of work suggests that integrating human creativity, intuition, and contextual understanding with AI's speed, scalability, and analytical power can lead to innovative solutions and improved decision-making in areas such as healthcare [1], customer service [2], and scientific research [3]. On the other hand, a growing number of studies reveal that human-AI systems do not necessarily achieve better results when compared to the best of humans or AI alone. Challenges such as communication barriers, trust issues, ethical concerns, and the need for effective coordination between humans and AI systems can hinder the collaborative process [4-9]. These seemingly contradictory results raise important questions: When do humans and AI complement each other?

Artificial intelligence (AI) can assess far more data far more quickly than any single human can do. With such immense pools of information, AI should be able to consider past data, process all the implications and produce a reliable prediction better than a human--right? That may not always be the case, according to a multi-institution research team who examined the synergies between how humans and AI make predictions. They publish their results on Aug. 23 in Journal of Social Computing. "Predictive tasks are ubiquitous--any decision-making in any field or facet of life involves predicting the consequences of the available options before choosing them," said paper author Scott E. Page, professor at University of Michigan's Ross Business School.

As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people will ask whether their robotic partners are trustworthy and effective collaborators. This begs a crucial question: how can we quantitatively measure the helpfulness of a robotic partner for a given task at hand? This paper seeks to answer this question with regards to the interactive robot's decision making. We describe a clear, concise, and task-oriented metric applicable to many different planning and execution paradigms. The proposed helpfulness metric is fundamental to assessing the benefit that a partner has on a team for a given task. In this paper, we define helpfulness, illustrate it on concrete examples from a variety of domains, discuss its properties and ramifications for planning interactions with humans, and present preliminary results.

Researchers from the University of Southern California's (USC) Viterbi Information Sciences Institute (ISI) are working hard on being able to predict the future. Imagine knowing when an earthquake was going to hit a region a month in advance? Or what the daily closing price of the Nikkei would be at the end of the week? Life would be a very different place, and that's exactly what the USC team has been working on by combining Artificial Intelligence (AI) and human predicting. Aram Galstyan and his team at USC ISI have been working on the Synergistic Anticipation of Geopolitical Events (SAGE) project for two years, in a way to predict the future without needing any experts.

The British startup Exscientia claims it has developed the first medication created using artificial intelligence that will be clinically tested on humans. The medication, which is meant to treat obsessive-compulsive disorder, took less than a year from conception to trial-ready capsule. Human trials are set to begin in March, but would you take a drug designed using artificially intelligent software? The appeal of AI-designed drugs is relatively straightforward. There are lots and lots of possible molecules that might be useful in medications, far too many for all the medical researchers in the world to manually test.

The British startup Exscientia claims it has developed the first medication created using artificial intelligence that will be clinically tested on humans. The medication, which is meant to treat obsessive-compulsive disorder, took less than a year from conception to trial-ready capsule. Human trials are set to begin in March, but would you take a drug designed using artificially intelligent software? The appeal of AI-designed drugs is relatively straightforward. There are lots and lots of possible molecules that might be useful in medications, far too many for all the medical researchers in the world to manually test.